1. Field of the Invention
The present invention relates to a reference data optimization learning method for adding reference data for a pattern matching process to be recognized, and a pattern recognition system for recognizing/learning the reference data by using this method.
2. Description of the Related Art
In the pattern recognition system, the pattern matching process is an important factor for a recognition process. According to a conventional system, a mode realized based on a processor is most common. This model employs a method for sequentially comparing data of input patterns with a plurality of reference data in a memory to detect reference data indicating the most similar patterns. However, the method of this model has a problem in that the sequential comparison necessitates much processing time, and the time for the processing is increased in proportion to an increase in the number of comparison data. Regarding the reference data, due to the sequential comparison, there is no learning concept in changing of a comparison order or registration of new reference data, making it impossible to obtain a processing time reduction effect by learning.
Pattern recognition and learning are functions essential for building an artificial intelligence system which has capabilities similar to those of the human brain. Accordingly, it is technically very significant to realize such functions in an integrated circuit. Most of methods proposed thus far to realize pattern recognition and learning functions use a neural network. An example is described in Document 1 below.
[Document 1] Yoshihito Amamiya, “Neural Network LSI”, Institute of Electronics, Information and Communication Engineers, 1995.
However, the method using the neural network has had a problem in that a threshold value of each neuron and a load of each network must be updated necessitating a great deal of processing time. As there is no method available to efficiently store patterns in the network, information of the patterns must be embraced in a network structure itself. Besides, to learn to recognize a new pattern after completion of network learning, relearning of the entire network is necessary, and it is difficult to update the load of the network by learning while carrying out a recognition process.
Recent progress in the realization method by hardware using a neural network has not been as fast as originally expected, and it is now difficult to realize an LSI in terms of degree of integration and power consumption. Under these circumstances, there is a demand for a better realization method by new hardware which can efficiently store patterns by incorporating a memory element.
With the foregoing in mind, development of a small-area/high-speed associative memory based on a fully parallel architecture has recently been pursued. Examples are described in detail in Documents 2 to 8 below.
[Document 2] H. J. Mattausch, N, Omori, S. Fukae, T. Koide, and T. Gyoten, “Fully-parallel pattern-matching engine with dynamic adaptability to Hamming or Manhattan distance”, 2002 Symposium on VLSI Circuit Dig. of Tech. Papers, pp. 252-255, 2002.
[Document 3] T. Koide, Y. Yano, H. J. Mattausch, “An associative memory for real-time applications requiring fully parallel nearest Manhattan-distance-search”, Proc. of the Workshop on Synthesis And System Integration of Mixed Technologies, pp. 200-205, 2003.
[Document 4] H. J. Mattaush. T. Gyohten, Y. Soda, T. Koide, “Compact associative-memory architecture with fully-parallel search capability for the minimum Hamming distance”, IEEE Journal of Solid-State Circuits, Vol. 37, pp. 218-227, 2002.
[Document 5] Y. Yano, T. Koide, H. J. Mattausch, “Fully parallel nearest Manhattan-distance search memory with large reference-pattern number”, Extend. Abst. of the International Conf. on Solid-State Devices and Materials, pp. 254-255, 2002.
[Document 6] Jpn. Pat. Appln. KOKAI Publication. No. 2001-011760
[Document 7] Jpn. Pat. Appln. KOKAI Publication. No. 2002-159436
[Document 8] Jpn. Pat. Appln. KOKAI Publication No. 2002-165759
The associative memory has a smallest distance search function which is an important element for realizing a recognition function, and can make parallel comparison among all data by comparing search data with each reference data based on digital comparison calculation and analog smallest value calculation. Accordingly, the associative memory is attracting attention as it can realize a high-speed pattern matching process.
However, even in the case of the pattern recognition system based on the associative memory, a specific method is yet to be established which enables efficient learning to recognize new reference data, and a process of adding or replacing reference data needs a great deal of time.